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  2. Ramsey RESET test - Wikipedia

    en.wikipedia.org/wiki/Ramsey_RESET_test

    The test was developed by James B. Ramsey as part of his Ph.D. thesis at the University of Wisconsin–Madison in 1968, and later published in the Journal of the Royal Statistical Society in 1969. [ 1 ] [ 2 ]

  3. Error correction model - Wikipedia

    en.wikipedia.org/wiki/Error_correction_model

    This can be done by standard unit root DF testing and ADF test (to resolve the problem of serially correlated errors). Take the case of two different series and . If both are I(0), standard regression analysis will be valid.

  4. White test - Wikipedia

    en.wikipedia.org/wiki/White_test

    White test is a statistical test that establishes whether the variance of the errors in a regression model is constant: that is for homoskedasticity. This test, and an estimator for heteroscedasticity-consistent standard errors , were proposed by Halbert White in 1980. [ 1 ]

  5. Statistical model specification - Wikipedia

    en.wikipedia.org/.../Statistical_model_specification

    There are several different possible causes of specification error; some are listed below. An inappropriate functional form could be employed. A variable omitted from the model may have a relationship with both the dependent variable and one or more of the independent variables (causing omitted-variable bias ).

  6. Durbin–Wu–Hausman test - Wikipedia

    en.wikipedia.org/wiki/Durbin–Wu–Hausman_test

    The Hausman test can be used to differentiate between fixed effects model and random effects model in panel analysis.In this case, Random effects (RE) is preferred under the null hypothesis due to higher efficiency, while under the alternative Fixed effects (FE) is at least as consistent and thus preferred.

  7. Errors-in-variables model - Wikipedia

    en.wikipedia.org/wiki/Errors-in-variables_model

    Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.

  8. Breusch–Godfrey test - Wikipedia

    en.wikipedia.org/wiki/Breusch–Godfrey_test

    The Breusch–Godfrey test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic is derived from these. The null hypothesis is that there is no serial correlation of any order up to p. [3]

  9. Regression testing - Wikipedia

    en.wikipedia.org/wiki/Regression_testing

    Regression testing is performed when changes are made to the existing functionality of the software or if there is a bug fix in the software. Regression testing can be achieved through multiple approaches; if a test all approach is followed, it provides certainty that the changes made to the software have not affected the existing functionalities, which are unaltered.